Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations

• URL: http://arxiv.org/abs/2303.08362v1
• Date: Wed, 15 Mar 2023 04:46:57 GMT
• Title: Transfer Learning Based Diagnosis and Analysis of Lung Sound Aberrations
• Authors: Hafsa Gulzar, Jiyun Li, Arslan Manzoor, Sadaf Rehmat, Usman Amjad and Hadiqa Jalil Khan
• Abstract summary: This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. Respiratory Sound Database obtained cutting-edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%.
• Score: 0.35232085374661276
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of computer -systems that can collect and analyze enormous volumes of data, the medical profession is establishing several non-invasive tools. This work attempts to develop a non-invasive technique for identifying respiratory sounds acquired by a stethoscope and voice recording software via machine learning techniques. This study suggests a trained and proven CNN-based approach for categorizing respiratory sounds. A visual representation of each audio sample is constructed, allowing resource identification for classification using methods like those used to effectively describe visuals. We used a technique called Mel Frequency Cepstral Coefficients (MFCCs). Here, features are retrieved and categorized via VGG16 (transfer learning) and prediction is accomplished using 5-fold cross-validation. Employing various data splitting techniques, Respiratory Sound Database obtained cutting-edge results, including accuracy of 95%, precision of 88%, recall score of 86%, and F1 score of 81%. The ICBHI dataset is used to train and test the model.

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